When Thursday, June 2, 2016 from 8:30 AM to 5:00 PM (AEST) - Add to Calendar

Where Data61 - Ground floor seminar room 7 London Circuit, Canberra, ACT - View Map

Cost: Free

Registration: Via https://www.eventbrite.com/e/nvidia-deep-learning-roadshow-downunder-edition-canberra-tickets-25507315066

Deep learning is the fastest-growing field in artificial intelligence (AI) and machine learning (ML). It is used in the research community and in industry to tackle some of the most challenging big data problems in computer vision, speech recognition, and natural language processing.

AI researchers and data scientists now have access to a wealth of hardware and software platforms, all powered by NVIDIA GPUs for training and deployment of neural networks. This workshop will give you a chance to experience some of those tools, and introduce NVIDIA's first supercomputer in a box: the NVIDIA DGX-1™ Deep Learning System.

In association with CSIRO Data61 and NCI, we present the Deep Learning Roadshow Downunder Edition (Canberra). Below you will find a full day technical workshop from our line-up of international deep learning experts, as well as guest speakers from CSIRO Data61 and the NCI.

You will see that several sessions have been left up to the audience to decide.

During the registration process, you may nominate from a number of available topics, including questions you would like our experts to consider prior to the workshop, as well as hands-on components (these require a laptop with internet connection, and may involve working in a group).

We look forwawrd to welcoming you to the Deep Learning Roadshow!

Agenda

• 8:30 AM - Arrival and registration
• 9:00 AM - Welcome
• 9:15 AM - NVIDIA Deep Learning SDK
• 10:00 AM - NVIDIA DGX-1™ Deep Learning Supercomputer
• 10:30 AM - Accelerating Science and Innovation at CSIRO (Dr John Taylor, CSIRO Data61)
• 10:45 AM - [Choose your session 1]
• 12:00 PM - Lunch
• 1:15 PM - GPUs for Deep Learning and HPC at NCI (Dr Muhammad Atif, NCI)
• 1:30 PM - Large-scale scene understanding targeting resource constrained platforms (Dr Jose Alvarez, CSIRO Data61)
• 2:00 PM - [Choose your session 2]
• 3:00 PM - [Choose your session 3]
• Depending on session choices, we will finish between 4-5pm.

Guest experts bios

John Taylor

Dr. John Taylor currently leads CSIRO Data61 Computational Platforms Group and the Computational and Simulation Sciences Future Science Platform. John has written more than 150 articles and books on computational and simulation science, climate change, global biogeochemical cycles, air quality and environmental policy, from the local to the global scale, spanning science, impacts and environmental policy. His research has been widely cited and attracted significant media attention. John has worked as a computational scientist and group leader both at the Mathematics and Computer Science Division, Argonne National Laboratory and at the Atmospheric Science Division at Lawrence Livermore National Laboratory. John was senior fellow in the Computation Institute at the University of Chicago. He has served on the Advisory Panel of the Scientific Computing Division of U.S. National Center for Atmospheric Research (NCAR) and the U.S. National Energy Research Scientific Computing Center NUGEX Advisory Committee. John currently serves on the board of the National eResearch Collaboration Tools and Resources (NeCTAR), a federal government super science initiative. 

Jose Alvarez

Dr. Jose M. Alvarez is a computer vision researcher at Data61 at CSIRO (formerly NICTA) in the Smart Vision Systems group (Australia) working on large-scale dynamic scene understanding and deep learning..

Dr. Alvarez graduated with his Ph.D. from Autonomous University of Barcelona (UAB) in October 2010.  While pursuing his Ph.D. at UAB (2006-2010) Dr. Alvarez was funded through research, teaching assistantships and industrial projects. During his Ph.D. program, Dr. Alvarez visited the ISLA group at the University of Amsterdam (in 2008 and 2009), and the Group Research Electronics at Volkswagen (in 2010). Dr. Alvarez was awarded the best Ph.D. Thesis award in 2010 from the Autonomous University of Barcelona. Subsequently, Dr. Alvarez worked as a postdoctoral researcher at the Courant Institute of Mathematical Science, New York University. Since 2014, Dr. Alvarez serves as associate editor for IEEE Transactions on Intelligent Transportation Systems.

Muhammad Atif

(coming soon...)

NVIDIA experts bios

Jon Barker

Jon Barker is a Solution Architect with NVIDIA, based in Boulder, CO. Jon helps customers and partners develop applications of GPU-accelerated machine learning and data analytics to solve defense and national security problems. He is particularly focused on applications of the rapidly developing field of deep learning. Prior to joining NVIDIA, Jon spent almost a decade as a government research scientist within the U.K. Ministry of Defence and the U.S. Department of Defense R&D communities. While in government service, he led R&D projects in sensor data fusion, big data analytics, and machine learning for multi-modal sensor data to support military situational awareness and aid decision making. He has a Ph.D. and B.Sc. in pure mathematics from the University of Southampton, U.K.

Pradeep Gupta

Pradeep Gupta is a Lead HPC & Deep Learning Solutions Architect at NVIDIA, where he supports customers and developers across Asia Pacific, Japan and India regions for Deep Learning and HPC application development. Pradeep also works to enable the GPU computing ecosystem in universities and research labs across region. Pradeep is also responsible for running and managing R&D Projects at NVIDIA Technology Centre at Singapore. Before joining NVIDIA, Pradeep had worked with various technologies in High Performance Computing at IBM and HP. Pradeep received a Master's degree in research from the Indian Institute of Science (IISc), Bangalore

Ryan Olson

Ryan Olson is a Solutions Architect in the Worldwide Field Organization at NVIDIA. His primary responsibilities involve supporting deep learning and high performance computing initiations. Ryan is particularly interested in scalable software design that leverages the unique capabilities of the underlying hardware. Prior to NVIDIA, Ryan spent 8 years working at Cray where he helped architect novel solutions that enabled applications to run at scale on some of the world’s largest supercomptuers including Oak Ridge National Lab’s Jaguar and Titan machines as well as the National Science Foundation’s Blue Waters machine at NCSA. Ryan holds a Ph.D. in Physical Chemistry from Iowa State University where he was a member of the Gordon Group working on the popular GAMESS chemistry package.

Ryan also spent a semester during his graduate work visiting Australian National University working with Alistair Rendell on novel hybrid communication methods for onesided frameworks.